This is a renewal application of 1R01CA126858, the parent grant titled Geospatial Factors and Impacts: Measurement and Use. Geographic disparities in breast cancer (BC) and colorectal cancer (CRC) prevention and outcomes have existed for decades. Their persistence may indicate inefficient use of healthcare resources in national and local efforts concerning cancer control prevention due to factors that differ between geographic areas. However, outside of our own work, spatial analytic methods have rarely been used in the past to combat these disparities. In the parent grant we enhanced software to analyze spatial data, made it widely available (to date used by 62,500 researchers), applied it to limited data sets, and demonstrated the impact of ignoring spatial effects in the analyses. In this new application, we will extend the existing software to incorporate new spatial analytic methods addressing two particular problems that were beyond the scope of the parent grant. We will make the new enhanced software widely available, and use newly available data to conduct comprehensive analyses. The results will provide actionable information to health planners that will improve the ability to reduce disparities in BC and CRC prevention and outcomes (i.e., cancer stage at time of cancer diagnosis), by using targeted interventions to identified populations in specific places. Using the enhanced software, we will apply the new methodological capabilities to estimate spatial relationships in the newly available national comprehensive cancer registry data, which cover all areas of the United States. These combined National Program of Cancer Registry/Surveillance Epidemiology and End Results (NPCR/SEER) registry data are restricted, person-level data (including cancer stage) available for approved projects inside the RDCs. We will conduct complex geospatial-multilevel analysis of U.S. BC and CRC populations, linking persons with cancer to their county, state, and regional characteristics. The new methods will allow for the proper incorporation of both spatial heterogeneity and spatial dependence in modeling the complex multilevel factors influencing disparities in cancer prevention and stage at diagnosis.